library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(plotly)
## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout
theme_full = 
  read_csv("ultimate data.csv")
## Rows: 920 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Park_Name, City, Country, Type, Region
## dbl (2): Year, Attendance
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Top 20 theme park from worldwide’s sum attendance by different country.

anova test by year between 2019 to 2022 \[H_0: \mu_{\text{2019}} = \mu_{\text{2020}} = \mu_{\text{2021}}= \mu_{\text{2022}} ~~ \text{vs} ~~ H_1: \text{at least two means are not equal}\]

dat = 
  theme_full |> 
  filter(
    Region != c("Worldwide")
    ) |> 
  mutate(
    Year = as.factor(Year)
  ) 

anova2 = aov(Attendance ~ Year , data = dat) |> 
  summary()

With a p-value of less than 2e-16, we would reject the null hypothesis. We have evidence that at least two of the means are not equal. Meaning the mean attendance among year groups is different for at least two groups.